Bloom filter based data collection algorithm for wireless sensor networks

Wireless sensor networks are emerging systems that can be used to monitor a variety of environments and communicate data to the relevant destination. This work scrutinizes the broadcast overhead problem in distributed sensor networks and propose a probabilistic structure (bloom filter) based technique, a new broadcast structure that attempts to reduce the number of duplicate copies of a packet at every node. This work shows that bloom based structure comes with a solution of a decreased energy consumption in the broadcast while achieving a full network coverage. The bloom filter is used for two purposes. First, to maintain the record of nodes requiring services from the central system in the form of an urgent member filter and communicate it reliably to the end node. Second, to create a neighbor filter. The unique idea of bloom based network uses a neighbor filter to incorporate the neighbor information on taking a forwarding decision and reduce broadcast overhead, i.e., the amount of duplication of packets at nodes. The simulation results show that use of bloom filter can achieve reduction in broadcast overhead by a minimum factor of 8 compared with the conventional broadcast system. In addition, it helps to reduce energy usage evenly throughout the network with 1/10 times and increases the lifetime of a network by having control over network density usage. The network density usage is compared with some existing broadcast control algorithms.